U.S. patent application number 16/026665 was filed with the patent office on 2020-01-09 for performance monitoring of system version releases.
The applicant listed for this patent is ServiceNow, Inc.. Invention is credited to Giora Sagy.
Application Number | 20200012493 16/026665 |
Document ID | / |
Family ID | 67396886 |
Filed Date | 2020-01-09 |
![](/patent/app/20200012493/US20200012493A1-20200109-D00000.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00001.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00002.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00003.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00004.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00005.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00006.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00007.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00008.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00009.png)
![](/patent/app/20200012493/US20200012493A1-20200109-D00010.png)
View All Diagrams
United States Patent
Application |
20200012493 |
Kind Code |
A1 |
Sagy; Giora |
January 9, 2020 |
PERFORMANCE MONITORING OF SYSTEM VERSION RELEASES
Abstract
A system and method for comparative performance monitoring of
software release versions is disclosed. A remote network management
platform may include a computational instance for managing a
network. Transactions between a server of the computational
instance and a client device in the managed network may be logged
to a database. Transactions may be carried out by a release version
of a set of program code units executing on the server. A software
application executing on a computing device may retrieve and
analyze a first set of transactions carried out by a first release
version of the set of program code units to determine a first set
of performance metrics, and do the same for a second set of
transactions carried out by a second release version of the set of
program code units to determine a second set of performance
metrics. A classification filter may be applied to the metrics, and
a quantitative comparison of the filtered first and second sets of
performance metrics may be displayed on graphical user device.
Inventors: |
Sagy; Giora; (Belvedere,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ServiceNow, Inc. |
Santa Clara |
CA |
US |
|
|
Family ID: |
67396886 |
Appl. No.: |
16/026665 |
Filed: |
July 3, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 43/08 20130101;
G06F 11/3688 20130101; H04L 43/045 20130101; H04L 41/0859 20130101;
G06F 8/71 20130101; H04L 43/028 20130101; G06F 11/3428 20130101;
G06F 11/323 20130101; G06F 2201/875 20130101; G06F 8/65 20130101;
G06F 8/77 20130101; G06F 2201/865 20130101 |
International
Class: |
G06F 8/71 20060101
G06F008/71; H04L 12/26 20060101 H04L012/26 |
Claims
1. A computing system comprising: a database disposed within a
remote network management platform, wherein the remote network
management platform comprises a computational instance associated
with a managed network, the database configured to log transactions
carried out between: (i) a release version of a set of program code
units executing on one or more server devices of the computational
instance, and (ii) one or more client devices associated with the
managed network; and a software application, configured to execute
on a computing device disposed within the remote network management
platform, further configured to: retrieve and analyze a first set
of transactions that were carried out by a first release version of
the set of program code units to determine a first set of
performance metrics, the first set of performance metrics including
first time-averaged completion rates of the first set of
transactions for each of a plurality of transaction
classifications; retrieve and analyze a second set of transactions
that were carried out by a second release version of the set of
program code units to determine a second set of performance
metrics, the second set of performance metrics including second
time-averaged completion rates of the second set of transactions
for each of the plurality of transaction classifications; receive,
from a user device, input specifying a classification filter to
apply to the plurality of transaction classifications of each of
the first and second sets of performance metrics; and provide, for
display on a graphical user interface (GUI) of the user device, a
quantitative comparison of the filtered first and second sets of
performance metrics.
2. The computing system of claim 1, wherein the transactions
comprise information indicative of a time-rate of completion for
each transaction.
3. The computing system of claim 2, wherein the transactions
further comprise: information identifying a respective group of one
or more web-based applications associated with each given
transaction; and information indicative of which of the first or
second versions of the set of program code units was operational
when the given transaction was carried out.
4. The computing system of claim 3, wherein each of the each of the
respective groups of the one or more web-based applications is
associated with a respective uniform record locator (URL) of a
website that provides one or more services relating to management
of the managed network.
5. The computing system of claim 3, wherein the first time-averaged
completion rates of the first set of transactions comprises
time-averaged completion rates of transactions associated with each
of the respective groups of the one or more web-based applications
as implemented by the first version of the set of program code
units, wherein the second time-averaged completion rates of the
second set of transactions comprises time-averaged completion rates
of transactions associated with each of the respective groups of
the one or more web-based applications as implemented by the second
version of the set of program code units, wherein each of the
respective groups of the one or more web-based applications
corresponds to one of the plurality of transaction classifications,
and wherein the quantitative comparison of the filtered first and
second sets of performance metrics comprises a difference between
the second and first time-averaged rates for one or more of the
respective groups of the one or more web-based applications.
6. The computing system of claim 5, wherein the quantitative
comparison of the filtered first and second sets of performance
metrics further comprises: a metric of an impact of the difference
between the second and first time-averaged rates for each of one or
more of the respective groups of the one or more web-based
applications, wherein the impact for each of the one or more of the
respective groups comprises the difference for the respective group
weighted by a frequency of occurrence of transactions of the
respective group, and each impact is added to a list of impacts for
the respective groups; and a ranking order of the list of impacts
according to relatives sizes of the impacts.
7. The computing system of claim 6, wherein providing the
quantitative comparison of the filtered first and second sets of
performance metrics comprises: providing at least a portion of the
list of impacts in ranking order as display elements, each display
element corresponding to a list entry and comprising the impact and
an identification of the respective group of the one or more
web-based applications associated with the list entry.
8. The computing system of claim 6, wherein each of the respective
groups of the one or more web-based applications is associated with
a respective filter category, each respective filter category being
one of: (i) a uniform record locator (URL) category, (ii) a network
management service category, (iii) a server device identifier
category, and (iv) a network resource category, and wherein the
ranking order of the list of impacts according to relatives sizes
of the impacts comprises: for a selected one or more of the
respective filter categories, a ranking order of an impact of
updating software of the one or more server devices of the
computational instance from the first version to the second version
of the set of program code units.
9. The computing system of claim 8, wherein the input specifying
the classification filter comprises selection criteria, the
selection criteria being at least one of: (i) a filter category, or
(ii) a ranking-order scheme, wherein the ranking-order scheme is
one of largest-to-smallest, smallest-to-largest, or
histogrammed.
10. The computing system of claim 1, wherein the computing system
further comprises a performance data repository, wherein
determining the first set of performance metrics comprises writing
the first set of performance metrics to the performance data
repository, and wherein determining the second set of performance
metrics comprises writing the second set of performance metrics to
the performance data repository.
11. A computing system comprising: a database disposed within a
remote network management platform, wherein the remote network
management platform comprises a plurality of computational
instances, each associated with a respective managed network, the
database configured to log transactions carried out between: (i) a
release version of a set of program code units executing on one or
more server devices of the computational instances, and (ii) one or
more client devices associated with the respective managed
networks; and a software application, configured to execute on a
computing device disposed within the remote network management
platform, and further configured to: retrieve and analyze a first
set of transactions that were carried out by a first release
version of the set of program code units to determine a first set
of performance metrics, the first set of performance metrics
including first time-averaged completion rates of the first set of
transactions for each of a plurality of transaction
classifications; retrieve and analyze a second set of transactions
that were carried out by a second release version of the set of
program code units to determine a second set of performance
metrics, the second set of performance metrics including second
time-averaged completion rates of the second set of transactions
for each of the plurality of transaction classifications; receive,
from a user device, input specifying a classification filter to
apply to the plurality of transaction classifications of each of
the first and second sets of performance metrics; and provide, for
display on a graphical user interface (GUI) of the user device, a
quantitative comparison of the filtered first and second sets of
performance metrics.
12. The computing system of claim 11, wherein the transactions
comprise: information indicative of a time-rate of completion for
each transaction; information identifying a respective group of one
or more web-based applications associated with each transaction;
information identifying which of the computational instances of the
plurality is associated with each transaction; and information
indicative of which of the first or second versions of the set of
program code units was operational when the given transaction was
carried out.
13. The computing system of claim 12, wherein each of the each of
the respective groups of the one or more web-based applications is
associated with a respective uniform record locator (URL) of a
website that provides one or more services relating to management
of the managed network.
14. The computing system of claim 12, wherein the first
time-averaged completion rates of the first set of transactions
comprise time-averaged completion rates of transactions associated
with each of the respective groups of the one or more web-based
applications as implemented by the first version of the set of
program code units, wherein the second time-averaged completion
rates of the second set of transactions comprise time-averaged
completion rates of transactions associated with each of the
respective groups of the one or more web-based applications as
implemented by the second version of the set of program code units,
wherein each of the respective groups of the one or more web-based
applications corresponds to one of the plurality of transaction
classifications, and wherein the quantitative comparison of the
filtered first and second sets of performance metrics comprises a
difference between the second and first time-averaged rates for one
or more of the respective groups of the one or more web-based
applications.
15. The computing system of claim 14, wherein the quantitative
comparison of the filtered first and second sets of performance
metrics further comprises: a metric of an impact of the difference
between the second and first time-averaged rates for each of one or
more of the respective groups of the one or more web-based
applications, wherein the impact for each of the one or more of the
respective groups is the difference for the respective group
weighted by a frequency of occurrence of transactions of the
respective group, and each impact is added to a list of impacts for
the respective groups, and a ranking order of the list of impacts
according to relatives sizes of the impacts; and wherein providing
the quantitative comparison of the filtered first and second sets
of performance metrics comprises: providing at least a portion of
the list of impacts in ranking order as display elements, each
display element corresponding to a list entry and comprising the
impact and an identification of the respective group of the one or
more web-based applications associated with the list entry.
16. The computing system of claim 15, wherein each of the
respective groups of the one or more web-based applications is
associated with a respective filter category, each respective
filter category being one of: (i) a uniform record locator (URL)
category, (ii) a computational instance category, (iii) a server
device identifier category, and (iv) a network resource category,
and wherein the ranking order of the list of impacts according to
relatives sizes of the impacts comprises: for a selected one or
more of the respective filter categories, a ranking order of an
impact of updating software of the one or more server devices of
the computational instance from the first version to the second
version of the set of program code units.
17. The computing system of claim 12, wherein the first
time-averaged completion rates of the first set of transactions
comprise time-averaged completion rates of the first set of
transactions for each of the computational instances, wherein the
second time-averaged completion rates of the second set of
transactions comprise time-averaged completion rates of the second
set of transactions for each of the computational instances,
wherein each of the computational instances corresponds to one of
the plurality of transaction classifications, and wherein the
quantitative comparison of the filtered first and second sets of
performance metrics comprises a difference between the second and
first time-averaged rates for one or more of the computational
instances.
18. The computing system of claim 17, wherein the quantitative
comparison of the filtered first and second sets of performance
metrics further comprises: a metric of an impact of the difference
between the second and first time-averaged rates for each of one or
more of the respective computational instances, wherein the impact
for each of the one or more of the respective computational
instances comprises the difference for the respective computational
instance weighted by a frequency of occurrence of transactions of
the respective computational instance, and each impact is added to
a list of impacts for the respective groups, and a ranking order of
the list of impacts according to relatives sizes of the impacts;
and wherein providing the quantitative comparison of the filtered
first and second sets of performance metrics comprises: providing
at least a portion of the list of impacts in ranking order as
display elements, each display element corresponding to a list
entry and comprising the impact and an identification of the
respective computational instance associated with the list
entry.
19. A method carried out by one or more computing devices disposed
within a remote network management platform, wherein the remote
network management platform comprises a computational instance
associated with a managed network, the method comprising: logging,
to a database of the remote network management platform,
transactions carried out between: (i) a release version of a set of
program code units executing on one or more server devices of the
computational instance, and (ii) one or more client devices
associated with the managed network; retrieving and analyzing a
first set of transactions that were carried out by a first release
version of the set of program code units to determine a first set
of performance metrics, the first set of performance metrics
including first time-averaged completion rates of the first set of
transactions for each of a plurality of transaction
classifications; retrieving and analyzing a second set of
transactions that were carried out by a second release version of
the set of program code units to determine a second set of
performance metrics, the second set of performance metrics
including second time-averaged completion rates of the second set
of transactions for each of the plurality of transaction
classifications; receiving, from a user device, input specifying a
classification filter to apply to the plurality of transaction
classifications of each of the first and second sets of performance
metrics; and providing, for display on a graphical user interface
(GUI) of the user device, a quantitative comparison of the filtered
first and second sets of performance metrics.
20. The method of claim 19, wherein the remote network management
platform comprises at least one additional computational instance,
each associated with a respective additional managed network, and
wherein the method further comprises: logging, to the database of
the remote network management platform, additional transactions
carried out between: (i) the set of program code units executing on
one or more server devices of the at least one additional
computational instance, and (ii) one or more client devices
associated with the respective additional managed network;
retrieving and analyzing an additional first set of the additional
transactions that were carried out by the first version of the set
of program code units to determine an additional first set of
performance metrics, the additional first set of performance
metrics including additional first time-averaged completion rates
of the additional first set of transactions for each of the
plurality of transaction classifications; retrieving and analyzing
an additional second set of the additional transactions that were
carried out by the second version of the set of program code units
to determine an additional second set of performance metrics, the
additional second set of performance metrics including additional
second time-averaged completion rates of the additional second set
of transactions for each of the plurality of transaction
classifications; and receiving, from the user device, input
specifying a further classification filter to apply to the
plurality of transaction classifications of each of: (i) an
aggregate of the first set of performance metrics and the
additional first set of performance metrics, and (ii) an aggregate
of the second set of performance metrics and the additional second
set of performance metrics; and providing, for display on the GUI
of the user device, a quantitative comparison of the filtered
aggregate first and aggregate second sets of performance metrics.
Description
BACKGROUND
[0001] Managed networks may include various types of computer
networks that can be remotely administered. This management may
involve one or more computing devices disposed within a remote
network management platform collecting information about the
configuration and operational states of software applications
executing on behalf on the managed network, and then presenting
representations of this information by way of one or more user
interfaces. The user interfaces may be, for instance, web-based
user interfaces. In some instances, remote management of networks
may be provided by a third party, such as a service provider or
vendor.
[0002] A remote network management platform may take the form of a
hosted environment that provides application Platform-as-a-Service
(aPaaS) services to users, particularly to operators of a managed
network such as enterprises. Such services may take the form of
web-based portals and/or software applications that enterprises,
and both internal and external users thereof, may access through
one or another form of deployment of the remote network management
platform.
[0003] A network management service provider or system vendor may
update or upgrade system software from time to time. The service
provider or vendor, as well as the customer or organizations whose
networks they manage, may be interested in assessing performance of
the updated or upgraded system.
SUMMARY
[0004] In accordance with example embodiments, a common remote
network management platform may implement individualized network
management for particular customers or organizations using a mix of
physical and/or logical components to build constructs referred to
herein as "computational instances." Operationally, a computational
instance may make a set of web portals, services, and applications
available to a particular customer. Both common and distinct
infrastructure components, such as servers, databases, and software
may be configured in an architecture that provides multiple
computational instances for serving multiple customers or
organizations. An enterprise or other entity can use a
computational instance to access various web-based resources (e.g.,
web pages) provided by the remote network management platform, as
well as other services. In an example deployment, a service
provider or vendor may own and/or operate a common remote network
management platform that includes multiple computational instances,
each associated with, and supporting network management services
for, a distinct enterprise, organization, or customer. At the level
of an end user of an enterprise or organization, web-based
resources may support mission-specific services or tasks, for
example.
[0005] In an attempt to access a web-based resource, a client
device of an enterprise may send a request to a server, and the
server may then processes the request and provide the web-based
resource to the client device. The act of the server processing the
request may involve the server executing one or more program code
units that define how the web-based resource operates, is accessed,
is designed, and/or the information it provides. By way of example,
program code units can include executable instructions, data (e.g.,
variables, constants, etc.), and configuration data. The server may
complete the request by sending a response to the client device,
and the client device may then utilize the information in the
response. For example, the information may be used to display a
webpage or other graphic output. In operation, a complete
request/response cycle may typically be considered a
transaction.
[0006] As a general matter, a given program code unit may be
deployed by the remote network management platform on the
computational instance by way of a software release for the
computational instance. A software release may correspond to a
version of the software, which may include multiple program code
units as part of the release. A service provider or vendor may
deploy some or all of a software release across multiple instances,
allowing same or similar services in each instance to be
implemented by a common set of program code units.
[0007] Every so often or from time to time, one or more
computational instances may be upgraded from one software release
to another, typically with an intent to add new features to the
computational instance and/or to improve existing features and/or
performance of the computational instance. These upgrades typically
include changes to program code units and/or other data that was
deployed on the computational instance in the previous software
release. That is, some collection of new or upgraded versions of
program code units may replace previous or older versions. The
"roll-out" of an upgrade may or may not necessarily be carried out
on all computational instances at the same time. But at some point
in time, each computational instance may run at least some common
set of program code units of the same version.
[0008] While upgrades are typically tested, verified, and validated
prior to roll-out, system performance may not necessarily improve
from one version to the next, depending, for example, on the
purpose of a new/upgraded version. Further, the detailed manner in
which a system software release operates in a production
environment, such as a computational instance supporting remote
network management for a given enterprise or organization, may
differ from that in which it operates in a testing environment
prior to roll-out. It may therefore be of interest to a service
provider or vendor and/or to an enterprise or other customer of the
service provider or vendor to be able to evaluate comparative
system performance before and after roll-out of a new or subsequent
release version of the system software.
[0009] In accordance with example embodiments, various performance
metrics of transactions, such as completion rates and server
response times, may be used carry out such a comparative
evaluation. More particularly, one or more metrics of the amount of
time it takes to complete transactions may be collected for system
operation under two or more versions of software releases. For
example, the metrics may be collected within different time
intervals during which different software release versions were
operational. Analyses and comparisons of the collected metrics may
then be used quantify the impact of upgrades or updates on system
performance. The comparisons may be made within individual
computational instances, and/or across computational instances.
[0010] Accordingly, a first example embodiment may involve a
computing system comprising: a database disposed within a remote
network management platform, wherein the remote network management
platform comprises a computational instance associated with a
managed network, the database configured to log transactions
carried out between: (i) a release version of a set of program code
units executing on one or more server devices of the computational
instance, and (ii) one or more client devices associated with the
managed network; and a software application, configured to execute
on a computing device disposed within the remote network management
platform, further configured to: retrieve and analyze a first set
of transactions that were carried out by a first release version of
the set of program code units to determine a first set of
performance metrics, the first set of performance metrics including
first time-averaged completion rates of the first set of
transactions for each of a plurality of transaction
classifications; retrieve and analyze a second set of transactions
that were carried out by a second release version of the set of
program code units to determine a second set of performance
metrics, the second set of performance metrics including second
time-averaged completion rates of the second set of transactions
for each of the plurality of transaction classifications; receive,
from a user device, input specifying a classification filter to
apply to the plurality of transaction classifications of each of
the first and second sets of performance metrics; and provide, for
display on a graphical user interface (GUI) of the user device, a
quantitative comparison of the filtered first and second sets of
performance metrics.
[0011] A second example embodiment may involve a computing system
comprising: a database disposed within a remote network management
platform, wherein the remote network management platform comprises
a plurality of computational instances, each associated with a
respective managed network, the database configured to log
transactions carried out between: (i) a release version of a set of
program code units executing on one or more server devices of the
computational instances, and (ii) one or more client devices
associated with the respective managed networks; and a software
application, configured to execute on a computing device disposed
within the remote network management platform, and further
configured to: retrieve and analyze a first set of transactions
that were carried out by a first release version of the set of
program code units to determine a first set of performance metrics,
the first set of performance metrics including first time-averaged
completion rates of the first set of transactions for each of a
plurality of transaction classifications; retrieve and analyze a
second set of transactions that were carried out by a second
release version of the set of program code units to determine a
second set of performance metrics, the second set of performance
metrics including second time-averaged completion rates of the
second set of transactions for each of the plurality of transaction
classifications; receive, from a user device, input specifying a
classification filter to apply to the plurality of transaction
classifications of each of the first and second sets of performance
metrics; and provide, for display on a graphical user interface
(GUI) of the user device, a quantitative comparison of the filtered
first and second sets of performance metrics.
[0012] In a third example embodiment may involve a method carried
out by one or more computing devices disposed within a remote
network management platform, wherein the remote network management
platform comprises a computational instance associated with a
managed network, the method comprising: logging, to a database of
the remote network management platform, transactions carried out
between: (i) a release version of a set of program code units
executing on one or more server devices of the computational
instance, and (ii) one or more client devices associated with the
managed network; retrieving and analyzing a first set of
transactions that were carried out by a first release version of
the set of program code units to determine a first set of
performance metrics, the first set of performance metrics including
first time-averaged completion rates of the first set of
transactions for each of a plurality of transaction
classifications; retrieving and analyzing a second set of
transactions that were carried out by a second release version of
the set of program code units to determine a second set of
performance metrics, the second set of performance metrics
including second time-averaged completion rates of the second set
of transactions for each of the plurality of transaction
classifications; receiving, from a user device, input specifying a
classification filter to apply to the plurality of transaction
classifications of each of the first and second sets of performance
metrics; and providing, for display on a graphical user interface
(GUI) of the user device, a quantitative comparison of the filtered
first and second sets of performance metrics.
[0013] In a fourth example embodiment, a system may include various
means for carrying out each of the operations of the first and/or
second example embodiment.
[0014] These as well as other embodiments, aspects, advantages, and
alternatives will become apparent to those of ordinary skill in the
art by reading the following detailed description, with reference
where appropriate to the accompanying drawings. Further, this
summary and other descriptions and figures provided herein are
intended to illustrate embodiments by way of example only and, as
such, that numerous variations are possible. For instance,
structural elements and process steps can be rearranged, combined,
distributed, eliminated, or otherwise changed, while remaining
within the scope of the embodiments as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 illustrates a schematic drawing of a computing
device, in accordance with example embodiments.
[0016] FIG. 2 illustrates a schematic drawing of a server device
cluster, in accordance with example embodiments.
[0017] FIG. 3 depicts a remote network management architecture, in
accordance with example embodiments.
[0018] FIG. 4 depicts a communication environment involving a
remote network management architecture, in accordance with example
embodiments.
[0019] FIG. 5A depicts another communication environment involving
a remote network management architecture, in accordance with
example embodiments.
[0020] FIG. 5B is a flow chart, in accordance with example
embodiments.
[0021] FIG. 6A depicts certain aspects of a remote network
management architecture relating to performance monitoring of
release versions, in accordance with example embodiments.
[0022] FIG. 6B is a conceptual illustration relating to performance
monitoring of release versions within a computational instance of a
remote network management architecture, in accordance with example
embodiments.
[0023] FIG. 6C is a conceptual illustration relating to performance
monitoring of release versions across a computational instances of
a remote network management architecture, in accordance with
example embodiments.
[0024] FIG. 7A depicts an example dashboard for performance
monitoring of release versions in a remote network management
architecture, in accordance with example embodiments.
[0025] FIG. 7A depicts an example dashboard for performance
monitoring of release versions in a remote network management
architecture, in accordance with example embodiments.
[0026] FIG. 7B depicts another example dashboard for performance
monitoring of release versions in a remote network management
architecture, in accordance with example embodiments.
[0027] FIG. 7C depicts yet another example dashboard for
performance monitoring of release versions in a remote network
management architecture, in accordance with example
embodiments.
[0028] FIG. 7D depicts still another example dashboard for
performance monitoring of release versions in a remote network
management architecture, in accordance with example
embodiments.
[0029] FIG. 8 is a flow chart, in accordance with example
embodiments.
DETAILED DESCRIPTION
[0030] Example methods, devices, and systems are described herein.
It should be understood that the words "example" and "exemplary"
are used herein to mean "serving as an example, instance, or
illustration." Any embodiment or feature described herein as being
an "example" or "exemplary" is not necessarily to be construed as
preferred or advantageous over other embodiments or features unless
stated as such. Thus, other embodiments can be utilized and other
changes can be made without departing from the scope of the subject
matter presented herein.
[0031] Accordingly, the example embodiments described herein are
not meant to be limiting. It will be readily understood that the
aspects of the present disclosure, as generally described herein,
and illustrated in the figures, can be arranged, substituted,
combined, separated, and designed in a wide variety of different
configurations. For example, the separation of features into
"client" and "server" components may occur in a number of ways.
[0032] Further, unless context suggests otherwise, the features
illustrated in each of the figures may be used in combination with
one another. Thus, the figures should be generally viewed as
component aspects of one or more overall embodiments, with the
understanding that not all illustrated features are necessary for
each embodiment.
[0033] Additionally, any enumeration of elements, blocks, or steps
in this specification or the claims is for purposes of clarity.
Thus, such enumeration should not be interpreted to require or
imply that these elements, blocks, or steps adhere to a particular
arrangement or are carried out in a particular order.
I. Introduction
[0034] A large enterprise is a complex entity with many
interrelated operations. Some of these are found across the
enterprise, such as human resources (HR), supply chain, information
technology (IT), and finance. However, each enterprise also has its
own unique operations that provide essential capabilities and/or
create competitive advantages.
[0035] To support widely-implemented operations, enterprises
typically use off-the-shelf software applications, such as customer
relationship management (CRM) and human capital management (HCM)
packages. However, they may also need custom software applications
to meet their own unique requirements. A large enterprise often has
dozens or hundreds of these custom software applications.
Nonetheless, the advantages provided by the embodiments herein are
not limited to large enterprises and may be applicable to an
enterprise, or any other type of organization, of any size.
[0036] Many such software applications are developed by individual
departments within the enterprise. These range from simple
spreadsheets to custom-built software tools and databases. But the
proliferation of siloed custom software applications has numerous
disadvantages. It negatively impacts an enterprise's ability to run
and grow its business, innovate, and meet regulatory requirements.
The enterprise may find it difficult to integrate, streamline and
enhance its operations due to lack of a single system that unifies
its subsystems and data.
[0037] To efficiently create custom applications, enterprises would
benefit from a remotely-hosted application platform that eliminates
unnecessary development complexity. The goal of such a platform
would be to reduce time-consuming, repetitive application
development tasks so that software engineers and individuals in
other roles can focus on developing unique, high-value
features.
[0038] In order to achieve this goal, the concept of Application
Platform as a Service (aPaaS) is introduced, to intelligently
automate workflows throughout the enterprise. An aPaaS system is
hosted remotely from the enterprise, but may access data,
applications, and services within the enterprise by way of secure
connections. Such an aPaaS system may have a number of advantageous
capabilities and characteristics. These advantages and
characteristics may be able to improve the enterprise's operations
and workflow for IT, HR, CRM, customer service, application
development, and security.
[0039] The aPaaS system may support development and execution of
model-view-controller (MVC) applications. MVC applications divide
their functionality into three interconnected parts (model, view,
and controller) in order to isolate representations of information
from the manner in which the information is presented to the user,
thereby allowing for efficient code reuse and parallel development.
These applications may be web-based, and offer create, read,
update, delete (CRUD) capabilities. This allows new applications to
be built on a common application infrastructure.
[0040] The aPaaS system may support standardized application
components, such as a standardized set of widgets for graphical
user interface (GUI) development. In this way, applications built
using the aPaaS system have a common look and feel. Other software
components and modules may be standardized as well. In some cases,
this look and feel can be branded or skinned with an enterprise's
custom logos and/or color schemes.
[0041] The aPaaS system may support the ability to configure the
behavior of applications using metadata. This allows application
behaviors to be rapidly adapted to meet specific needs. Such an
approach reduces development time and increases flexibility.
Further, the aPaaS system may support GUI tools that facilitate
metadata creation and management, thus reducing errors in the
metadata.
[0042] The aPaaS system may support clearly-defined interfaces
between applications, so that software developers can avoid
unwanted inter-application dependencies. Thus, the aPaaS system may
implement a service layer in which persistent state information and
other data is stored.
[0043] The aPaaS system may support a rich set of integration
features so that the applications thereon can interact with legacy
applications and third-party applications. For instance, the aPaaS
system may support a custom employee-onboarding system that
integrates with legacy HR, IT, and accounting systems.
[0044] The aPaaS system may support enterprise-grade security.
Furthermore, since the aPaaS system may be remotely hosted, it
should also utilize security procedures when it interacts with
systems in the enterprise or third-party networks and services
hosted outside of the enterprise. For example, the aPaaS system may
be configured to share data amongst the enterprise and other
parties to detect and identify common security threats.
[0045] Other features, functionality, and advantages of an aPaaS
system may exist. This description is for purpose of example and is
not intended to be limiting.
[0046] As an example of the aPaaS development process, a software
developer may be tasked to create a new application using the aPaaS
system. First, the developer may define the data model, which
specifies the types of data that the application uses and the
relationships therebetween. Then, via a GUI of the aPaaS system,
the developer enters (e.g., uploads) the data model. The aPaaS
system automatically creates all of the corresponding database
tables, fields, and relationships, which can then be accessed via
an object-oriented services layer.
[0047] In addition, the aPaaS system can also build a
fully-functional MVC application with client-side interfaces and
server-side CRUD logic. This generated application may serve as the
basis of further development for the user. Advantageously, the
developer does not have to spend a large amount of time on basic
application functionality. Further, since the application may be
web-based, it can be accessed from any Internet-enabled client
device. Alternatively or additionally, a local copy of the
application may be able to be accessed, for instance, when Internet
service is not available.
[0048] The aPaaS system may also support a rich set of pre-defined
functionality that can be added to applications. These features
include support for searching, email, templating, workflow design,
reporting, analytics, social media, scripting, mobile-friendly
output, and customized GUIs.
[0049] The following embodiments describe architectural and
functional aspects of example aPaaS systems, as well as the
features and advantages thereof.
II. Example Computing Devices and Cloud-Based Computing
Environments
[0050] FIG. 1 is a simplified block diagram exemplifying a
computing device 100, illustrating some of the components that
could be included in a computing device arranged to operate in
accordance with the embodiments herein. Computing device 100 could
be a client device (e.g., a device actively operated by a user), a
server device (e.g., a device that provides computational services
to client devices), or some other type of computational platform.
Some server devices may operate as client devices from time to time
in order to perform particular operations, and some client devices
may incorporate server features.
[0051] In this example, computing device 100 includes processor
102, memory 104, network interface 106, and an input/output unit
108, all of which may be coupled by a system bus 110 or a similar
mechanism. In some embodiments, computing device 100 may include
other components and/or peripheral devices (e.g., detachable
storage, printers, and so on).
[0052] Processor 102 may be one or more of any type of computer
processing element, such as a central processing unit (CPU), a
co-processor (e.g., a mathematics, graphics, or encryption
co-processor), a digital signal processor (DSP), a network
processor, and/or a form of integrated circuit or controller that
performs processor operations. In some cases, processor 102 may be
one or more single-core processors. In other cases, processor 102
may be one or more multi-core processors with multiple independent
processing units. Processor 102 may also include register memory
for temporarily storing instructions being executed and related
data, as well as cache memory for temporarily storing recently-used
instructions and data.
[0053] Memory 104 may be any form of computer-usable memory,
including but not limited to random access memory (RAM), read-only
memory (ROM), and non-volatile memory (e.g., flash memory, hard
disk drives, solid state drives, compact discs (CDs), digital video
discs (DVDs), and/or tape storage). Thus, memory 104 represents
both main memory units, as well as long-term storage. Other types
of memory may include biological memory.
[0054] Memory 104 may store program instructions and/or data on
which program instructions may operate. By way of example, memory
104 may store these program instructions on a non-transitory,
computer-readable medium, such that the instructions are executable
by processor 102 to carry out any of the methods, processes, or
operations disclosed in this specification or the accompanying
drawings.
[0055] As shown in FIG. 1, memory 104 may include firmware 104A,
kernel 104B, and/or applications 104C. Firmware 104A may be program
code used to boot or otherwise initiate some or all of computing
device 100. Kernel 104B may be an operating system, including
modules for memory management, scheduling and management of
processes, input/output, and communication. Kernel 104B may also
include device drivers that allow the operating system to
communicate with the hardware modules (e.g., memory units,
networking interfaces, ports, and busses), of computing device 100.
Applications 104C may be one or more user-space software programs,
such as web browsers or email clients, as well as any software
libraries used by these programs. Memory 104 may also store data
used by these and other programs and applications.
[0056] Network interface 106 may take the form of one or more
wireline interfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit
Ethernet, and so on). Network interface 106 may also support
communication over one or more non-Ethernet media, such as coaxial
cables or power lines, or over wide-area media, such as Synchronous
Optical Networking (SONET) or digital subscriber line (DSL)
technologies. Network interface 106 may additionally take the form
of one or more wireless interfaces, such as IEEE 802.11 (Wifi),
BLUETOOTH.RTM., global positioning system (GPS), or a wide-area
wireless interface. However, other forms of physical layer
interfaces and other types of standard or proprietary communication
protocols may be used over network interface 106. Furthermore,
network interface 106 may comprise multiple physical interfaces.
For instance, some embodiments of computing device 100 may include
Ethernet, BLUETOOTH.RTM., and Wifi interfaces.
[0057] Input/output unit 108 may facilitate user and peripheral
device interaction with example computing device 100. Input/output
unit 108 may include one or more types of input devices, such as a
keyboard, a mouse, a touch screen, and so on. Similarly,
input/output unit 108 may include one or more types of output
devices, such as a screen, monitor, printer, and/or one or more
light emitting diodes (LEDs). Additionally or alternatively,
computing device 100 may communicate with other devices using a
universal serial bus (USB) or high-definition multimedia interface
(HDMI) port interface, for example.
[0058] In some embodiments, one or more instances of computing
device 100 may be deployed to support an aPaaS architecture. The
exact physical location, connectivity, and configuration of these
computing devices may be unknown and/or unimportant to client
devices. Accordingly, the computing devices may be referred to as
"cloud-based" devices that may be housed at various remote data
center locations.
[0059] FIG. 2 depicts a cloud-based server cluster 200 in
accordance with example embodiments. In FIG. 2, operations of a
computing device (e.g., computing device 100) may be distributed
between server devices 202, data storage 204, and routers 206, all
of which may be connected by local cluster network 208. The number
of server devices 202, data storages 204, and routers 206 in server
cluster 200 may depend on the computing task(s) and/or applications
assigned to server cluster 200.
[0060] For example, server devices 202 can be configured to perform
various computing tasks of computing device 100. Thus, computing
tasks can be distributed among one or more of server devices 202.
To the extent that these computing tasks can be performed in
parallel, such a distribution of tasks may reduce the total time to
complete these tasks and return a result. For purpose of
simplicity, both server cluster 200 and individual server devices
202 may be referred to as a "server device." This nomenclature
should be understood to imply that one or more distinct server
devices, data storage devices, and cluster routers may be involved
in server device operations.
[0061] Data storage 204 may be data storage arrays that include
drive array controllers configured to manage read and write access
to groups of hard disk drives and/or solid state drives. The drive
array controllers, alone or in conjunction with server devices 202,
may also be configured to manage backup or redundant copies of the
data stored in data storage 204 to protect against drive failures
or other types of failures that prevent one or more of server
devices 202 from accessing units of cluster data storage 204. Other
types of memory aside from drives may be used.
[0062] Routers 206 may include networking equipment configured to
provide internal and external communications for server cluster
200. For example, routers 206 may include one or more
packet-switching and/or routing devices (including switches and/or
gateways) configured to provide (i) network communications between
server devices 202 and data storage 204 via cluster network 208,
and/or (ii) network communications between the server cluster 200
and other devices via communication link 210 to network 212.
[0063] Additionally, the configuration of cluster routers 206 can
be based at least in part on the data communication requirements of
server devices 202 and data storage 204, the latency and throughput
of the local cluster network 208, the latency, throughput, and cost
of communication link 210, and/or other factors that may contribute
to the cost, speed, fault-tolerance, resiliency, efficiency and/or
other design goals of the system architecture.
[0064] As a possible example, data storage 204 may include any form
of database, such as a structured query language (SQL) database.
Various types of data structures may store the information in such
a database, including but not limited to tables, arrays, lists,
trees, and tuples. Furthermore, any databases in data storage 204
may be monolithic or distributed across multiple physical
devices.
[0065] Server devices 202 may be configured to transmit data to and
receive data from cluster data storage 204. This transmission and
retrieval may take the form of SQL queries or other types of
database queries, and the output of such queries, respectively.
Additional text, images, video, and/or audio may be included as
well. Furthermore, server devices 202 may organize the received
data into web page representations. Such a representation may take
the form of a markup language, such as the hypertext markup
language (HTML), the extensible markup language (XML), or some
other standardized or proprietary format. Moreover, server devices
202 may have the capability of executing various types of
computerized scripting languages, such as but not limited to Perl,
Python, PHP Hypertext Preprocessor (PHP), Active Server Pages
(ASP), JavaScript, and so on. Computer program code written in
these languages may facilitate the providing of web pages to client
devices, as well as client device interaction with the web
pages.
III. Example Remote Network Management Architecture
[0066] FIG. 3 depicts a remote network management architecture, in
accordance with example embodiments. This architecture includes
three main components, managed network 300, remote network
management platform 320, and third-party networks 340, all
connected by way of Internet 350.
[0067] Managed network 300 may be, for example, an enterprise
network used by a business for computing and communications tasks,
as well as storage of data. Thus, managed network 300 may include
various client devices 302, server devices 304, routers 306,
virtual machines 308, firewall 310, and/or proxy servers 312.
Client devices 302 may be embodied by computing device 100, server
devices 304 may be embodied by computing device 100 or server
cluster 200, and routers 306 may be any type of router, switch, or
gateway.
[0068] Virtual machines 308 may be embodied by one or more of
computing device 100 or server cluster 200. In general, a virtual
machine is an emulation of a computing system, and mimics the
functionality (e.g., processor, memory, and communication
resources) of a physical computer. One physical computing system,
such as server cluster 200, may support up to thousands of
individual virtual machines. In some embodiments, virtual machines
308 may be managed by a centralized server device or application
that facilitates allocation of physical computing resources to
individual virtual machines, as well as performance and error
reporting. Enterprises often employ virtual machines in order to
allocate computing resources in an efficient, as needed fashion.
Providers of virtualized computing systems include VMWARE.RTM. and
MICROSOFT.RTM..
[0069] Firewall 310 may be one or more specialized routers or
server devices that protect managed network 300 from unauthorized
attempts to access the devices, applications, and services therein,
while allowing authorized communication that is initiated from
managed network 300. Firewall 310 may also provide intrusion
detection, web filtering, virus scanning, application-layer
gateways, and other applications or services. In some embodiments
not shown in FIG. 3, managed network 300 may include one or more
virtual private network (VPN) gateways with which it communicates
with remote network management platform 320 (see below).
[0070] Managed network 300 may also include one or more proxy
servers 312. An embodiment of proxy servers 312 may be a server
device that facilitates communication and movement of data between
managed network 300, remote network management platform 320, and
third-party networks 340. In particular, proxy servers 312 may be
able to establish and maintain secure communication sessions with
one or more computational instances of remote network management
platform 320. By way of such a session, remote network management
platform 320 may be able to discover and manage aspects of the
architecture and configuration of managed network 300 and its
components. Possibly with the assistance of proxy servers 312,
remote network management platform 320 may also be able to discover
and manage aspects of third-party networks 340 that are used by
managed network 300.
[0071] Firewalls, such as firewall 310, typically deny all
communication sessions that are incoming by way of Internet 350,
unless such a session was ultimately initiated from behind the
firewall (i.e., from a device on managed network 300) or the
firewall has been explicitly configured to support the session. By
placing proxy servers 312 behind firewall 310 (e.g., within managed
network 300 and protected by firewall 310), proxy servers 312 may
be able to initiate these communication sessions through firewall
310. Thus, firewall 310 might not have to be specifically
configured to support incoming sessions from remote network
management platform 320, thereby avoiding potential security risks
to managed network 300.
[0072] In some cases, managed network 300 may consist of a few
devices and a small number of networks. In other deployments,
managed network 300 may span multiple physical locations and
include hundreds of networks and hundreds of thousands of devices.
Thus, the architecture depicted in FIG. 3 is capable of scaling up
or down by orders of magnitude.
[0073] Furthermore, depending on the size, architecture, and
connectivity of managed network 300, a varying number of proxy
servers 312 may be deployed therein. For example, each one of proxy
servers 312 may be responsible for communicating with remote
network management platform 320 regarding a portion of managed
network 300. Alternatively or additionally, sets of two or more
proxy servers may be assigned to such a portion of managed network
300 for purposes of load balancing, redundancy, and/or high
availability.
[0074] Remote network management platform 320 is a hosted
environment that provides aPaaS services to users, particularly to
the operators of managed network 300. These services may take the
form of web-based portals, for instance. Thus, a user can securely
access remote network management platform 320 from, for instance,
client devices 302, or potentially from a client device outside of
managed network 300. By way of the web-based portals, users may
design, test, and deploy applications, generate reports, view
analytics, and perform other tasks.
[0075] As shown in FIG. 3, remote network management platform 320
includes four computational instances 322, 324, 326, and 328. Each
of these instances may represent a set of web portals, services,
and applications (e.g., a wholly-functioning aPaaS system)
available to a particular customer. In some cases, a single
customer may use multiple computational instances. For example,
managed network 300 may be an enterprise customer of remote network
management platform 320, and may use computational instances 322,
324, and 326. The reason for providing multiple instances to one
customer is that the customer may wish to independently develop,
test, and deploy its applications and services. Thus, computational
instance 322 may be dedicated to application development related to
managed network 300, computational instance 324 may be dedicated to
testing these applications, and computational instance 326 may be
dedicated to the live operation of tested applications and
services. A computational instance may also be referred to as a
hosted instance, a remote instance, a customer instance, or by some
other designation.
[0076] The multi-instance architecture of remote network management
platform 320 is in contrast to conventional multi-tenant
architectures, over which multi-instance architectures have several
advantages. In multi-tenant architectures, data from different
customers (e.g., enterprises) are comingled in a single database.
While these customers' data are separate from one another, the
separation is enforced by the software that operates the single
database. As a consequence, a security breach in this system may
impact all customers' data, creating additional risk, especially
for entities subject to governmental, healthcare, and/or financial
regulation. Furthermore, any database operations that impact one
customer will likely impact all customers sharing that database.
Thus, if there is an outage due to hardware or software errors,
this outage affects all such customers. Likewise, if the database
is to be upgraded to meet the needs of one customer, it will be
unavailable to all customers during the upgrade process. Often,
such maintenance windows will be long, due to the size of the
shared database.
[0077] In contrast, the multi-instance architecture provides each
customer with its own database in a dedicated computing instance.
This prevents comingling of customer data, and allows each instance
to be independently managed. For example, when one customer's
instance experiences an outage due to errors or an upgrade, other
computational instances are not impacted. Maintenance down time is
limited because the database only contains one customer's data.
Further, the simpler design of the multi-instance architecture
allows redundant copies of each customer database and instance to
be deployed in a geographically diverse fashion. This facilitates
high availability, where the live version of the customer's
instance can be moved when faults are detected or maintenance is
being performed.
[0078] In order to support multiple computational instances in an
efficient fashion, remote network management platform 320 may
implement a plurality of these instances on a single hardware
platform. For example, when the aPaaS system is implemented on a
server cluster such as server cluster 200, it may operate a virtual
machine that dedicates varying amounts of computational, storage,
and communication resources to instances. But full virtualization
of server cluster 200 might not be necessary, and other mechanisms
may be used to separate instances. In some examples, each instance
may have a dedicated account and one or more dedicated databases on
server cluster 200. Alternatively, computational instance 322 may
span multiple physical devices.
[0079] In some cases, a single server cluster of remote network
management platform 320 may support multiple independent
enterprises. Furthermore, as described below, remote network
management platform 320 may include multiple server clusters
deployed in geographically diverse data centers in order to
facilitate load balancing, redundancy, and/or high
availability.
[0080] Third-party networks 340 may be remote server devices (e.g.,
a plurality of server clusters such as server cluster 200) that can
be used for outsourced computational, data storage, communication,
and service hosting operations. These servers may be virtualized
(i.e., the servers may be virtual machines). Examples of
third-party networks 340 may include AMAZON WEB SERVICES.RTM. and
MICROSOFT.RTM. Azure. Like remote network management platform 320,
multiple server clusters supporting third-party networks 340 may be
deployed at geographically diverse locations for purposes of load
balancing, redundancy, and/or high availability.
[0081] Managed network 300 may use one or more of third-party
networks 340 to deploy applications and services to its clients and
customers. For instance, if managed network 300 provides online
music streaming services, third-party networks 340 may store the
music files and provide web interface and streaming capabilities.
In this way, the enterprise of managed network 300 does not have to
build and maintain its own servers for these operations.
[0082] Remote network management platform 320 may include modules
that integrate with third-party networks 340 to expose virtual
machines and managed services therein to managed network 300. The
modules may allow users to request virtual resources and provide
flexible reporting for third-party networks 340. In order to
establish this functionality, a user from managed network 300 might
first establish an account with third-party networks 340, and
request a set of associated resources. Then, the user may enter the
account information into the appropriate modules of remote network
management platform 320. These modules may then automatically
discover the manageable resources in the account, and also provide
reports related to usage, performance, and billing.
[0083] Internet 350 may represent a portion of the global Internet.
However, Internet 350 may alternatively represent a different type
of network, such as a private wide-area or local-area
packet-switched network.
[0084] FIG. 4 further illustrates the communication environment
between managed network 300 and computational instance 322, and
introduces additional features and alternative embodiments. In FIG.
4, computational instance 322 is replicated across data centers
400A and 400B. These data centers may be geographically distant
from one another, perhaps in different cities or different
countries. Each data center includes support equipment that
facilitates communication with managed network 300, as well as
remote users.
[0085] In data center 400A, network traffic to and from external
devices flows either through VPN gateway 402A or firewall 404A. VPN
gateway 402A may be peered with VPN gateway 412 of managed network
300 by way of a security protocol such as Internet Protocol
Security (IPSEC) or Transport Layer Security (TLS). Firewall 404A
may be configured to allow access from authorized users, such as
user 414 and remote user 416, and to deny access to unauthorized
users. By way of firewall 404A, these users may access
computational instance 322, and possibly other computational
instances. Load balancer 406A may be used to distribute traffic
amongst one or more physical or virtual server devices that host
computational instance 322. Load balancer 406A may simplify user
access by hiding the internal configuration of data center 400A,
(e.g., computational instance 322) from client devices. For
instance, if computational instance 322 includes multiple physical
or virtual computing devices that share access to multiple
databases, load balancer 406A may distribute network traffic and
processing tasks across these computing devices and databases so
that no one computing device or database is significantly busier
than the others. In some embodiments, computational instance 322
may include VPN gateway 402A, firewall 404A, and load balancer
406A.
[0086] Data center 400B may include its own versions of the
components in data center 400A. Thus, VPN gateway 402B, firewall
404B, and load balancer 406B may perform the same or similar
operations as VPN gateway 402A, firewall 404A, and load balancer
406A, respectively. Further, by way of real-time or near-real-time
database replication and/or other operations, computational
instance 322 may exist simultaneously in data centers 400A and
400B.
[0087] Data centers 400A and 400B as shown in FIG. 4 may facilitate
redundancy and high availability. In the configuration of FIG. 4,
data center 400A is active and data center 400B is passive. Thus,
data center 400A is serving all traffic to and from managed network
300, while the version of computational instance 322 in data center
400B is being updated in near-real-time. Other configurations, such
as one in which both data centers are active, may be supported.
[0088] Should data center 400A fail in some fashion or otherwise
become unavailable to users, data center 400B can take over as the
active data center. For example, domain name system (DNS) servers
that associate a domain name of computational instance 322 with one
or more Internet Protocol (IP) addresses of data center 400A may
re-associate the domain name with one or more IP addresses of data
center 400B. After this re-association completes (which may take
less than one second or several seconds), users may access
computational instance 322 by way of data center 400B.
[0089] FIG. 4 also illustrates a possible configuration of managed
network 300. As noted above, proxy servers 312 and user 414 may
access computational instance 322 through firewall 310. Proxy
servers 312 may also access configuration items 410. In FIG. 4,
configuration items 410 may refer to any or all of client devices
302, server devices 304, routers 306, and virtual machines 308, any
applications or services executing thereon, as well as
relationships between devices, applications, and services. Thus,
the term "configuration items" may be shorthand for any physical or
virtual device, or any application or service remotely discoverable
or managed by computational instance 322, or relationships between
discovered devices, applications, and services. Configuration items
may be represented in a configuration management database (CMDB) of
computational instance 322.
[0090] As noted above, VPN gateway 412 may provide a dedicated VPN
to VPN gateway 402A. Such a VPN may be helpful when there is a
significant amount of traffic between managed network 300 and
computational instance 322, or security policies otherwise suggest
or require use of a VPN between these sites. In some embodiments,
any device in managed network 300 and/or computational instance 322
that directly communicates via the VPN is assigned a public IP
address. Other devices in managed network 300 and/or computational
instance 322 may be assigned private IP addresses (e.g., IP
addresses selected from the 10.0.0.0-10.255.255.255 or
192.168.0.0-192.168.255.255 ranges, represented in shorthand as
subnets 10.0.0.0/8 and 192.168.0.0/16, respectively).
IV. Example Device, Application, and Service Discovery
[0091] In order for remote network management platform 320 to
administer the devices, applications, and services of managed
network 300, remote network management platform 320 may first
determine what devices are present in managed network 300, the
configurations and operational statuses of these devices, and the
applications and services provided by the devices, and well as the
relationships between discovered devices, applications, and
services. As noted above, each device, application, service, and
relationship may be referred to as a configuration item. The
process of defining configuration items within managed network 300
is referred to as discovery, and may be facilitated at least in
part by proxy servers 312.
[0092] For purpose of the embodiments herein, an "application" may
refer to one or more processes, threads, programs, client modules,
server modules, or any other software that executes on a device or
group of devices. A "service" may refer to a high-level capability
provided by multiple applications executing on one or more devices
working in conjunction with one another. For example, a high-level
web service may involve multiple web application server threads
executing on one device and accessing information from a database
application that executes on another device.
[0093] FIG. 5A provides a logical depiction of how configuration
items can be discovered, as well as how information related to
discovered configuration items can be stored. For sake of
simplicity, remote network management platform 320, third-party
networks 340, and Internet 350 are not shown.
[0094] In FIG. 5A, CMDB 500 and task list 502 are stored within
computational instance 322. Computational instance 322 may transmit
discovery commands to proxy servers 312. In response, proxy servers
312 may transmit probes to various devices, applications, and
services in managed network 300. These devices, applications, and
services may transmit responses to proxy servers 312, and proxy
servers 312 may then provide information regarding discovered
configuration items to CMDB 500 for storage therein. Configuration
items stored in CMDB 500 represent the environment of managed
network 300.
[0095] Task list 502 represents a list of activities that proxy
servers 312 are to perform on behalf of computational instance 322.
As discovery takes place, task list 502 is populated. Proxy servers
312 repeatedly query task list 502, obtain the next task therein,
and perform this task until task list 502 is empty or another
stopping condition has been reached.
[0096] To facilitate discovery, proxy servers 312 may be configured
with information regarding one or more subnets in managed network
300 that are reachable by way of proxy servers 312. For instance,
proxy servers 312 may be given the IP address range 192.168.0/24 as
a subnet. Then, computational instance 322 may store this
information in CMDB 500 and place tasks in task list 502 for
discovery of devices at each of these addresses.
[0097] FIG. 5A also depicts devices, applications, and services in
managed network 300 as configuration items 504, 506, 508, 510, and
512. As noted above, these configuration items represent a set of
physical and/or virtual devices (e.g., client devices, server
devices, routers, or virtual machines), applications executing
thereon (e.g., web servers, email servers, databases, or storage
arrays), relationships therebetween, as well as services that
involve multiple individual configuration items.
[0098] Placing the tasks in task list 502 may trigger or otherwise
cause proxy servers 312 to begin discovery. Alternatively or
additionally, discovery may be manually triggered or automatically
triggered based on triggering events (e.g., discovery may
automatically begin once per day at a particular time).
[0099] In general, discovery may proceed in four logical phases:
scanning, classification, identification, and exploration. Each
phase of discovery involves various types of probe messages being
transmitted by proxy servers 312 to one or more devices in managed
network 300. The responses to these probes may be received and
processed by proxy servers 312, and representations thereof may be
transmitted to CMDB 500. Thus, each phase can result in more
configuration items being discovered and stored in CMDB 500.
[0100] In the scanning phase, proxy servers 312 may probe each IP
address in the specified range of IP addresses for open
Transmission Control Protocol (TCP) and/or User Datagram Protocol
(UDP) ports to determine the general type of device. The presence
of such open ports at an IP address may indicate that a particular
application is operating on the device that is assigned the IP
address, which in turn may identify the operating system used by
the device. For example, if TCP port 135 is open, then the device
is likely executing a WINDOWS.RTM. operating system. Similarly, if
TCP port 22 is open, then the device is likely executing a
UNIX.RTM. operating system, such as LINUX.RTM.. If UDP port 161 is
open, then the device may be able to be further identified through
the Simple Network Management Protocol (SNMP). Other possibilities
exist. Once the presence of a device at a particular IP address and
its open ports have been discovered, these configuration items are
saved in CMDB 500.
[0101] In the classification phase, proxy servers 312 may further
probe each discovered device to determine the version of its
operating system. The probes used for a particular device are based
on information gathered about the devices during the scanning
phase. For example, if a device is found with TCP port 22 open, a
set of UNIX.RTM.-specific probes may be used. Likewise, if a device
is found with TCP port 135 open, a set of WINDOWS.RTM.-specific
probes may be used. For either case, an appropriate set of tasks
may be placed in task list 502 for proxy servers 312 to carry out.
These tasks may result in proxy servers 312 logging on, or
otherwise accessing information from the particular device. For
instance, if TCP port 22 is open, proxy servers 312 may be
instructed to initiate a Secure Shell (SSH) connection to the
particular device and obtain information about the operating system
thereon from particular locations in the file system. Based on this
information, the operating system may be determined. As an example,
a UNIX.RTM. device with TCP port 22 open may be classified as
AIX.RTM., HPUX, LINUX.RTM., MACOS.RTM., or SOLARIS.RTM.. This
classification information may be stored as one or more
configuration items in CMDB 500.
[0102] In the identification phase, proxy servers 312 may determine
specific details about a classified device. The probes used during
this phase may be based on information gathered about the
particular devices during the classification phase. For example, if
a device was classified as LINUX.RTM., a set of LINUX.RTM.-specific
probes may be used. Likewise if a device was classified as
WINDOWS.RTM. 2012, as a set of WINDOWS.RTM.-2012-specific probes
may be used. As was the case for the classification phase, an
appropriate set of tasks may be placed in task list 502 for proxy
servers 312 to carry out. These tasks may result in proxy servers
312 reading information from the particular device, such as basic
input/output system (BIOS) information, serial numbers, network
interface information, media access control address(es) assigned to
these network interface(s), IP address(es) used by the particular
device and so on. This identification information may be stored as
one or more configuration items in CMDB 500.
[0103] In the exploration phase, proxy servers 312 may determine
further details about the operational state of a classified device.
The probes used during this phase may be based on information
gathered about the particular devices during the classification
phase and/or the identification phase. Again, an appropriate set of
tasks may be placed in task list 502 for proxy servers 312 to carry
out. These tasks may result in proxy servers 312 reading additional
information from the particular device, such as processor
information, memory information, lists of running processes
(applications), and so on. Once more, the discovered information
may be stored as one or more configuration items in CMDB 500.
[0104] Running discovery on a network device, such as a router, may
utilize SNMP. Instead of or in addition to determining a list of
running processes or other application-related information,
discovery may determine additional subnets known to the router and
the operational state of the router's network interfaces (e.g.,
active, inactive, queue length, number of packets dropped, etc.).
The IP addresses of the additional subnets may be candidates for
further discovery procedures. Thus, discovery may progress
iteratively or recursively.
[0105] Once discovery completes, a snapshot representation of each
discovered device, application, and service is available in CMDB
500. For example, after discovery, operating system version,
hardware configuration and network configuration details for client
devices, server devices, and routers in managed network 300, as
well as applications executing thereon, may be stored. This
collected information may be presented to a user in various ways to
allow the user to view the hardware composition and operational
status of devices, as well as the characteristics of services that
span multiple devices and applications.
[0106] Furthermore, CMDB 500 may include entries regarding
dependencies and relationships between configuration items. More
specifically, an application that is executing on a particular
server device, as well as the services that rely on this
application, may be represented as such in CMDB 500. For instance,
suppose that a database application is executing on a server
device, and that this database application is used by a new
employee onboarding service as well as a payroll service. Thus, if
the server device is taken out of operation for maintenance, it is
clear that the employee onboarding service and payroll service will
be impacted. Likewise, the dependencies and relationships between
configuration items may be able to represent the services impacted
when a particular router fails.
[0107] In general, dependencies and relationships between
configuration items be displayed on a web-based interface and
represented in a hierarchical fashion. Thus, adding, changing, or
removing such dependencies and relationships may be accomplished by
way of this interface.
[0108] Furthermore, users from managed network 300 may develop
workflows that allow certain coordinated activities to take place
across multiple discovered devices. For instance, an IT workflow
might allow the user to change the common administrator password to
all discovered LINUX.RTM. devices in single operation.
[0109] In order for discovery to take place in the manner described
above, proxy servers 312, CMDB 500, and/or one or more credential
stores may be configured with credentials for one or more of the
devices to be discovered. Credentials may include any type of
information needed in order to access the devices. These may
include userid/password pairs, certificates, and so on. In some
embodiments, these credentials may be stored in encrypted fields of
CMDB 500. Proxy servers 312 may contain the decryption key for the
credentials so that proxy servers 312 can use these credentials to
log on to or otherwise access devices being discovered.
[0110] The discovery process is depicted as a flow chart in FIG.
5B. At block 520, the task list in the computational instance is
populated, for instance, with a range of IP addresses. At block
522, the scanning phase takes place. Thus, the proxy servers probe
the IP addresses for devices using these IP addresses, and attempt
to determine the operating systems that are executing on these
devices. At block 524, the classification phase takes place. The
proxy servers attempt to determine the operating system version of
the discovered devices. At block 526, the identification phase
takes place. The proxy servers attempt to determine the hardware
and/or software configuration of the discovered devices. At block
528, the exploration phase takes place. The proxy servers attempt
to determine the operational state and applications executing on
the discovered devices. At block 530, further editing of the
configuration items representing the discovered devices and
applications may take place. This editing may be automated and/or
manual in nature.
[0111] The blocks represented in FIG. 5B are for purpose of
example. Discovery may be a highly configurable procedure that can
have more or fewer phases, and the operations of each phase may
vary. In some cases, one or more phases may be customized, or may
otherwise deviate from the exemplary descriptions above.
V. Example Comparative Performance of Software Release Versions
[0112] An enterprise or other entity associated with managed
network 300 may use a computational instance (e.g., computational
instance 322) of the remote network management platform 320 to
access various web-based resources provided by the remote network
management platform 320. A "web-based resource" may refer to any
data or program code (e.g., a web page or other information)
accessible by way of a transaction between a client device 302 and
a web server (e.g., a web server application executing on a web
server), where the web server is disposed within the computational
instance 322. Further, a "transaction" may refer to any request
transmitted from the client device 302 to the web server in an
attempt to access a web-based resource. Upon receipt of such a
request, the web server may process the request and, if the attempt
is successful, the web server may provide a representation of the
requested web-based resource to the client device 302.
[0113] For example, the enterprise may use a web browser on the
client device 302 to enter a request to load a web page of the
remote network management platform's web portal, and the web server
may responsively provide the web page for display. As another
example, the enterprise can request access to a web-based resource
using a representational state transfer (REST) application
programming interface (API) of the computational instance 322.
Through this REST API, the client device 302 may engage in
Hypertext Transfer Protocol (HTTP) communication with the web
server to gain access to the web-based resource. The act of
requesting access to the web-based resource may be referred to as a
REST API call, and may involve the client device 302 transmitting a
request to the web server in the form of a URL or other string
identifying the web-based resource. Upon receipt of the request,
the web server may process the request and transmit, to the client
device 302, the web-based resource represented in HTML, XML,
JavaScript Object Notation (JSON), or some other format.
[0114] As noted above, when processing a request to access a
web-based resource, the web server can execute one or more program
code units--namely, executable code, scripts, and/or other
data--that define how the web-based resource operates, is accessed,
is designed, and/or the information it provides.
[0115] By way of example, a program code unit can take the form of
a record-based rule that defines actions that can be performed with
respect to a record stored in a database (e.g., data storage 204).
A record-based rule may be a server-side script that runs when a
record stored in a database is displayed, inserted, updated, or
deleted, or when a table in the database is queried. Record-based
rules can be used to perform a variety of actions, such as
specifying field values on a form that the user is updating,
displaying messages to the user, preventing the user from accessing
or modifying certain fields on a form, or preventing the user from
adding new records in the database when certain criteria are met.
Other actions are possible as well.
[0116] As another example, a program code unit can take the form of
a user interface (UI) page. A UI page may be client-side and/or
server-side code defining forms, dialogs, lists, or other UI
components that make up a custom web page associated with a
particular application or service. That code, when executed, may
cause the custom web page to be provided and may facilitate
subsequent interactions with the custom web page.
[0117] Additionally or alternatively, other such program code units
can include UI macros (e.g., discrete, custom scripted controls or
interfaces that can be added to a UI), UI actions (e.g., code
defining operations related to buttons, links, and context menu
items on forms and lists), UI policies (e.g., code or scripts that
define how the behavior of information on a form can change and/or
that define process flows for completing tasks), client scripts
(e.g., client-side JavaScript that runs in a web browser), data
policies (e.g., rules applied to data entered into the
computational instance or received through web services), and/or
script includes (e.g., server-side scripts that define a function
or class), among other possible program code units. Program code
units may also include or take the form of configuration data.
Variations of the program code units listed above are possible as
well. As executed by one or more server devices of a computational
instance, program code units may thus implement various web-based
services and applications provided by the computational
instance.
[0118] FIG. 6A depicts certain aspects of the remote network
management platform 320 that illustrates additional details of the
web-based functionality provided by computational instances
described above. For the sake of brevity in the figure, only
computational instances 322, 324, and 326 are shown. By way of
example, each includes the same server devices 606, which in turn
supports the same web apps 610 as implemented by the same program
code units 608. Thus, in this particular example, the three
computational instances shown are implemented as three virtual
systems by common servers executing common code units providing a
set of common web apps. Other configurations are possible as well.
For example, each computational instance could be supported by a
separate server or server cluster, each running separate instances
of the web apps implemented by separate instances of the code
units.
[0119] As also shown, the remote network management platform 320
includes a transaction database 600, a computing device 602, and a
performance data repository 604. As described below, these three
components, together with a user device 612, may provide the
operational basis for monitoring of comparative performance of
different release versions of software that implements the web
services of the computational instances. By way of example, the
user device 612, which could be a client device or workstation with
graphical user interface, is shown as being external to the remote
network management platform 320. This arrangement could correspond
to the user device being remote from the platform. Additionally or
alternatively, however, the user device could be part of the
platform; e.g., directly or locally connected.
[0120] The example illustration further shows the managed network
300 and client devices 302, which may access services of the
computational instance 322. Again for brevity, other components and
elements of the managed network 300 are omitted from the figure.
The client devices 302 may engage in transactions with web apps 610
executing on server devices 606. The double arrow connecting the
client devices 302 with the server devices 606 in the computational
instance 322 represents such transactions.
[0121] As described, the web apps 610 may be implemented by program
code units 608. In accordance with example embodiments, at any
given time or during a given interval, the program code units 608
may be part of a particular software release version. Typically, a
software release for one or more computational instances, such as
computational instances 322, 324, and 326, may include a variety of
standard program code units that are associated with the remote
network management platform 320 and that have been approved by an
entity (e.g., a service provider) associated with the remote
network management platform 320 for inclusion in that release. The
remote network management platform 320 may also support features
that enable enterprises to customize a computational instance by
adding new program code units and/or modifying existing program
code units (i.e., modifying a standard program code unit of a
previous software release, or making a subsequent modification to a
previously-modified standard program code unit). Similarly,
enterprises may add new database tables and/or modify entries in
existing database tables.
[0122] From time to time, one or more of the computational
instances may undergo an upgrade during which the one or more of
the computational instances are transitioned from one software
release version to a subsequent software release version--namely,
an upgrade from any previous software release preceding the
subsequent software release to the subsequent software release. For
example, the computational instance 322 may download an upgrade
file that defines various changes between the previous software
release and the subsequent software release, and the computational
instance 322 may then apply some or all of those changes to itself.
In line with the discussion above, such changes may include changes
to program code units (e.g., adding new program code units or
modifying existing program code units) and/or changes to other data
associated with such program code units (e.g., modifying or
deleting a database table that is queried when a record-based rule
script is run).
[0123] Changes from one release version to another may represent
relatively small changes, such as "patches" that may fix bugs or
other issues. Additionally or alternatively, a subsequent release
version may be a more substantive upgrade, including major
enhancements, new features, and functional redesigns, among other
changes. Customarily, the term "release version" may be more
typically associated with such substantive upgrades than with
patches and/or minor repairs or fixes. But in any case, the set of
software code units that implements the web apps and other programs
on the server devices are generally generated as part of a
well-defined organizational construct that specifies dates of
software builds, release descriptions, and other mechanisms of
quality control and verification. Thus, for the purposes of the
discussion herein, the terms "software release," "software release
version," or the like, will be used to describe both major and
minor upgrade/update features include in the associated set of
program code units.
[0124] Further, it may not necessarily be the case that all
computational instances are upgraded to a new release version at
the same time. That is, a new release version of software may be
rolled out simultaneously to all computational instances,
incrementally to computational instances, or in some other
asynchronous scheme. For example, as noted earlier, an enterprise
may have two computational instances: one for production and
another for testing. In such an arrangement, a new release may
first be rolled out on the computational instance used for testing,
and, later, after verification, rolled out in the production
environment. In practice, there may also be intervals of time in
which all computational instances are implementing the same release
version. For example, if the time between two successive release
versions is long compared with a roll-out and testing time, then
all computational instances may eventually have the same release
version, at least for some period of time, even if the roll-out was
asynchronous.
[0125] Prior to roll-out of a new or upgraded release version, the
upgraded software will typically be tested and verified. This may
involve subjecting the upgraded system to a suite of tests and use
cases, among other testing and verification activities, to help
eliminate (or at least nearly so) errors and/or bugs in the upgrade
program code units and the functions and operations they carry out.
In addition, performance of various aspects of the upgraded
software may be determined or predicted. More particularly,
performance may be evaluated or predicted by exercising the test
system with multiple use cases. In practice, however, there may be
more and different types of uses cases in the production
environments of the computational instances that support managed
networks of the business enterprises and/or organizations that are
the customers or subscribers of the service provider or vendor of
the remote network management platform. Consequently, performance
of a new release in a computational instance may differ from that
predicted during pre-release testing. And even to the extent that
pre-release testing may be a reasonably accurate predictor of
performance in the production environment, not all aspects of a new
release will necessarily even be expected to yield better
performance than the previous release that it replaces.
[0126] It can happen that when a new release is rolled-out, certain
operations and functions of the new release appear to perform worse
than the same or similar operations and functions of the previous
release (or even of one or more even earlier releases). Conversely,
performance for various operations and functions may improve (or
stay roughly the same) from one release to the next. Further, the
size and quality of performance changes from one release version to
another may vary within a computational instance according to the
specific web apps or other service features invoked, and/or may
vary across different instances. A service provider or vendor that
supplies and rolls out new release versions to the computing
instances that support the managed networks of its customers (e.g.,
business enterprises, organizations, etc.) may therefore be
interested in being able to systematically and analytically compare
performance of new release versions with one or more prior release
versions. Customers of the service provider or vendor may also be
interested in systematic comparisons. Such a comparison may be
carried out between a rolled-out new release version and one or
more prior ones, or between any two prior ones, and may be useful
whether performance of one or more aspects of a system improves or
degrades between release versions.
[0127] In accordance with example embodiments, comparative
performance of two or more release versions of remote network
management systems made operational at different, respective times
on one or more computational instances may be achieved by
monitoring and logging transactions carried out in the system at
the different, respective times, and analyzing the logged
transactions to determine performance metrics from the different,
respective times that may then be compared. As described above, a
transaction involves a request transmitted from a client device 302
in a managed network 300 to a server device 606 in a computational
instance (e.g., computational instance 322), and a response
transmitted back from the server device, following execution of
instructions by server to process the request and generate and/or
aggregate information for the response. The amount of time to
complete a transaction is referred to herein as the transaction
"response time," and may be measured effectively as the time
between transmission of the request from the client device and
return of the response to the client device. In practice, a
transaction response time may include a sum of shorter times for
carrying out various actions that make up the transaction.
Non-limiting examples of such actions include network transmission
times for the request and response messages, server processing time
for carrying out steps at the server, resource waiting time for
latency associated with waiting for necessary resources to be
available and/or allocated, and database access times for those
requests that may involve read/write operations with one or more
databases.
[0128] For purposes of the present discussion, and as a first,
reasonable approximation, server processing time will be assumed to
account for the most impactful or sensitive portion of the total
transaction response time. Accordingly, a comparison of the
transaction response times for a given transaction carried out by
two different release versions may provide a basis for a
quantitative comparison of the performance of the two release
versions, at least with respect to processing involved in the given
transaction. To the extent that server processing time accounts for
most of the response time, the difference in average response times
between two release versions for a given transaction may provide a
measure, then, of a change in server processing time resulting from
changes in the code units that execute the transaction on the
server.
[0129] However, other possible components of transaction response
time may also be considered in comparative performance evaluations
of release versions. As described below components of transaction
response times may be tracked or monitored in different ways by
different system components. In accordance with example
embodiments, "raw" transaction data may be logged for all
transactions during specified time intervals. These data may
include individual transactions as captured by monitoring
transmissions of requests and associated responses. At the same
time, or nearly so, resource usage within computational instances
or within the remote network management as a whole may be monitored
such that usage patterns may be correlated with monitored
transactions, at least within particular time frames. Resource
usage may include allocation of semaphores for access to shared
resources (e.g., databases), processing of database requests, and
processing policy enforcement, among other aspects.
[0130] Referring again to FIG. 6A, some or all transactions between
client devices 302 and the server devices 606 that implement the
computational instance 322 may be logged to the transaction
database 600. The dashed arrow pointing into the transaction
database 600 represents this logging. In accordance with example
embodiments, transactions may be logged continuously or during
designated times, such as during intervals identified or known to
be the busiest times of day. While logging during selective time
intervals may not capture every possible transaction, arranging the
interval(s) to be during busy periods may help ensure the most
accurate and/or relevant metrics are logged.
[0131] Also in accordance with example embodiments, each logged
transaction may include various pieces of information that may be
used, either directly or through some form of analytical
processing, as a measure of performance. As an example, time stamps
indicating when a request from a client device was sent to a server
device, and when the server device sent the response marking
completion of the transaction, can be used to determine the time to
complete the transaction (e.g., response time). In addition,
information may be included that identifies the client device, the
server device, the service or web application invoked by the
request from the client device, and a web or network address to
which the request was sent. Such additional information may be used
to correlate transactions with resource usage, which may be tracked
or monitored separately from transactions themselves. This example
list of information associated with a transaction and logged to the
transaction database is not limiting, and other information may be
included as well. For example, each logged transaction may also
identify the managed network, the computational instance, and/or an
enterprise or other organization or entity associated with the
transaction.
[0132] In example embodiments, the web application(s) invoked on
the server by the request may be identifiable according to an
associated web address, such as a uniform record locator (URL). In
the context of web-based applications, a URL may function not only
as a symbolic address of a web-based service or website hosting one
or more services, but also may encode or include ancillary
information used in processing the request. For example, a URL may
contain information relating to a file system path, a query string,
file navigation data, user information, and information specific to
a web-based application invoked. Thus, a URL can serve as a
specific identifier of a web-based service and server, such that
among a multiplicity of logged transactions, URLs may provide
effective and efficient "tags" for classification, sorting, and/or
filtering of transactions for comparative performance
evaluations.
[0133] More particularly, the specificity of URLs to particular
web-based applications that may be invoked by virtue of web
requests that include URLs may make URLs useful identifiers of
software code units that implement the web-based applications. From
the point of view of a service provider or vendor, a particular URL
can be associated with particular software code units. As such,
performance analysis of services or functions accessed via the
particular URL may treat the URL as a proxy identifier of the
software code units whose performance is being evaluated. In
accordance with example embodiments, then, comparison of
performance associated with like URLs of different software release
versions can form a basis for comparison of the corresponding code
units of the different release versions.
[0134] The illustration of FIG. 6A appears to show logging of only
those transactions between the client device 302 in the managed
network 300 and the server 606 and web apps 610 in the
computational instance 322. However, in example embodiments,
transactions between client devices in other managed networks
associated with other computational instances 324, 326, and so on
(as indicated by the horizontal ellipses) may also be logged to the
transaction database 600. Thus, the transaction data base 600 may
serve as a repository for some or all transactions involving some
or all computational instances. The logged transactions may
therefore be considered a form of raw performance data. The
identifying information stored with each transaction may then be
used to classify, sort, and/or filter the data at any stage of
phase of further analysis, as described below.
[0135] In an example embodiment, the logging of transactions to the
transaction database 600 may be accomplished using a standard
logging facility, such as "syslog." As is known, syslog provides a
standard and uniform interface for logging data from disparate
types of systems and devices. In the present context of comparative
performance monitoring of software release versions, syslog may
facilitate a sort of "funnel" through which all (or some)
transactions pass, so that they may be captured in the transaction
database 600. Other standard and/or proprietary logging facilities
could be used as well or instead of syslog, and the reference to
syslog as a suitable logging facility is not intended to be
limiting with respect to example embodiments herein.
[0136] In accordance with example embodiments, the computing device
602 may access the logged transactions in the transaction database
600 and perform various levels of analysis to generate performance
metrics that may be used for comparing performance of two or more
release versions for which transactions have been logged.
Performance metrics may then be stored in the performance data
repository 604 for further analysis and interactive examination and
evaluation. In example embodiments, certain aspects of the
processing for generating the performance metrics may be automated,
while others may be invoked by users via the user device 612, for
example.
[0137] In practice, the number of total transactions executed or
carried out across all client devices in all managed networks
associated with all computational instances may grow quite large.
And in many practical cases, comparative performance of different
release versions may be appropriately accomplished by way of
statistical analysis of performance metrics and trends. For
example, average response times or transaction rates per unit time
during busy hours may be more revealing of performance that
individual response times. By way of example, the time interval for
computing averages response times or transaction rates could be one
minute. However, other intervals could be used as well, such as
every 30 seconds, or every 2, 5, or 10 minutes, for example. Other
time intervals for computing time-averaged rates, response time,
and other time-based metrics may be used as well.
[0138] From time to time, for example upon storing analyzed data
from the transaction database 600 to the performance data
repository 604, the transaction database 600 may be subject to
overwriting by new incoming logged transactions. With such an
arrangement, the transaction database 600 may serve as a sort of
temporary repository of raw transaction data. However, some portion
of raw transaction data may remain stored in the transaction
database 600 past the time that those data have been analyzed and
stored in the performance data repository 604. Other arrangements
are possible as well.
[0139] Applying statistical analysis, such as time averaging, may
be useful for determining and/or evaluating trends. For example,
completion times and response times for individual transactions may
be influenced by transient events in the network or the server. As
such, metrics for individual transactions may not be representative
of performance of the underlying code, but rather of possibly
unrelated impediments or effects. Time averaging and other
statistical techniques can help smooth over events that might
otherwise be misinterpreted as related to performance.
[0140] In example embodiments, computations of time averages of
transaction rates, response times, or other time-based metrics of
transaction performance may also be categorized or classified
according to other identifying information associated with the
logged transactions analyzed. More particularly, time averages
within and/or across classifications may be used to compare a
variety of aspects of different software release versions. For
example, performance of two or more software releases may be
compared based on different URLs within a single computational
instance, or based on the same URL across computational instances.
In the former case, different URLs within a single instance for
could be ranked URLs according to performance improvement or
degradation before and after a software upgrade. This could be used
to help evaluate the effect of the upgrade on different URLs (and
the associated code units), and the analysis could be performed for
each individual computational instance. In the latter case,
performance of a given URL could be ranked according to
computational instances before and after a software upgrade. This
could help evaluate the effect of the upgrade of the given URL on
different computational instances.
[0141] As another example, overall performance of two or more
software release versions may be compared across computational
instances. This could provide a more coarse-grained evaluation of
software upgrades on a per-computational-instance basis, without
necessarily accounting for individual URLs. Similar analyses of
performance to those described above may be carried out with
respect to specific server devices and/or user end-point devices in
addition to or as alternatives to URLs and/or computational
instances. These are just some examples.
[0142] As described above, the associated data stored with the
logged transactions, including, but not limited to, web
applications, URLs, server devices, computational instances, client
devices, and software release version designations, may serve as
categories or classes that may be used for selection when
evaluating comparative performance of release versions. In
accordance with example embodiments, the selection process may be
implemented as a filter, where the classes or categories are filter
criteria. For example, a filter could specify a comparison between
two particular release versions, and further specify a set of URLs
of each release version and one or more computational instances. It
will be appreciated that a filter may be constructed from any one
or more classes or categories of information associated with the
logged transactions.
[0143] The performance data repository 604 may also serve as a
repository for resource usage data. Additionally or alternatively,
usage data may be tracked and/or monitored by some other database
not necessarily shown in FIG. 6A. As described above, resource
usage and/or allocation maybe correlated with transactions using
identifying information in the transactions, such as associated web
applications or URLs, time stamps, and embedded database queries,
among other types of information. The ability to correlate
transactions with other system resources may provide for richer
analysis of comparative performance of release versions. Monitored
information relating to resource usage may also serve as additional
classes or categories for filtering. For example, a filter could
include semaphore usage or queueing of semaphore requests. Other
filter elements connected with resource usage could be used as
well.
[0144] While time-averaged rates of transactions, completion times,
response times, and the like, may serve as first-level metrics of
performance, as described, other higher-level metrics may be
derived as well, and may provide additional bases for evaluating
performance. In particular, various weighting factors and/or
functions may be applied to time-average-based metrics in order to
further reveal or assess the relative quantitative importance of
version changes to performance within one or more classes specified
by a filter. In accordance with example embodiments, performance
changes between two release versions may be measured by a metric
referred to herein as "impact." In example embodiments, for any
filter grouping of transactions, the impact of the difference of
performance between two release versions may be computed as the
difference in average response times (or time-averaged completion
times) between the two release versions multiplied by the number of
transactions of the filter grouping carried out for the later of
the two release versions.
[0145] As defined above, the impact accounts not only for a
difference in average completion times or rates of transactions
between two release versions, but also on how big a factor the
difference is relative to overall performance. For example, if an
upgrade from a current to a new release version results in a 10%
increase in average transaction response time for a given URL, but
transactions involving the given URL is just 0.5% of all
transactions since the new release version, then the impact is just
0.05%. In a more specific example, if the average response time for
a given URL increases by one second in a new release version, and
the number of transactions during a busy period for the new release
version is 1,800, then the impact is 1,800 seconds, or 30 minutes.
As another example, if the average response time for a different
URL increases by 2.5 seconds in a new release version, but the
number of transactions during a busy period for the new release
version is 50, then the impact is just 125 seconds, or just over
two minutes. These examples show how impact may be used to evaluate
the relative performance of two release versions for the particular
URLs. This type of analysis may be extended to allow impact-ranking
among URLs. Further, it will be appreciated that similar analyses
may be applied to various filter configurations in order to rank
other classes, both within and across individual computational
instances, according to impact.
[0146] A. Example Vertical and Horizontal Views
[0147] As described above, ancillary information associated with
logged transactions may be used to configure a filter for selection
of transactions used in comparative evaluation of software release
versions. In accordance with example embodiments, filters may be
used to refine or tune comparisons within an individual
computational instance and across computational instances. For
purposes of the discussion herein, a comparison within an
individual computation instance is referred to as a "vertical view"
and a comparison across computational instances is referred to as a
"horizontal view." FIGS. 6B and 6C reproduce certain portions of
FIG. 6A to illustrate the concepts and utilities of each view.
[0148] In particular, FIG. 6B illustrates a vertical view through
the computational instance 322. The managed network 300 is omitted
from FIG. 6B, as are the transaction database 600 and performance
data repository 604; the computational instances 324 and 326 are
grayed-out. A broad double arrow vertically through the
computational instance 322 is a visual cue indicating the vertical
view. As indicated, the vertical view provides a per-instance
comparison of two or more software release versions, designated as
"Before vs. After Update" in the figure. Thus, by way of example,
the illustration corresponds to two release versions, one a
"before" and the other an "after." As such a comparison may be used
to evaluate the impact(s) of a system upgrade. It will be
appreciated, however, that the techniques described herein could be
applied to any two or more release versions, either present vs.
historical or two or more historical release versions.
[0149] As indicated in FIG. 6B, examples of before and after
evaluations may include comparisons on a per URL basis, ranked by
URL, per service basis, or ranked by service. These examples are
not intended to be limiting. A per URL comparison could be used,
for example, to help diagnose possible performance degradation of a
particular URL following an upgrade. Such an evaluation could allow
a system manager or other personnel to view the overall impact on
the particular URL of an upgrade, as well as examine transaction
components that contribute to the overall impact. These could
include before and after comparisons of average server processing
times, database access times, semaphore usage and queuing times,
and network transmission times, among other possible components. In
accordance with example embodiments, the contributions of each of
these components may be determined according time-averaged response
times for transactions associated with the particular URL as
captured in the transaction log 600 over time intervals before and
after the upgrade.
[0150] An evaluation ranked by URL may list URL according to the
size of the impact of the upgrade. Such an evaluation could be
used, for example, to identify potential problems associated with a
URL resulting from an upgrade. Of course, such a list could also be
used to highlight URLs that have improved performance as well. In
either case, a system manager or other personnel may gain a better
understanding of factors that may impact performance before and
after an upgrade (or comparative performance between different
release versions in general).
[0151] Similar evaluations and comparisons may be made based on the
service(s) invoked by particular transactions, the server that
carries out transactions, or system resources that are utilized as
part of servicing transactions. Evaluations may be extended to
include other filter selections as well.
[0152] FIG. 6C illustrates a horizontal view across the
computational instances 322, 324, and 326. Again, the managed
network 300 is omitted from FIG. 6C, as are the transaction
database 600 and performance data repository 604. A broad double
arrow horizontally through the computational instances 322, 324,
and 326 is a visual cue indicating the horizontal view. As
indicated, the horizontal view provides a cross-instance comparison
of two or more software release versions, again designated as
"Before vs. After Update" in the figure. Again, by way of example,
the illustration corresponds to a comparison the impact(s) of a
system upgrade, but in this case for the overall remote network
management platform 320 in this case. Once more, the techniques
described herein could be applied to any two or more release
versions, either present vs. historical or two or more historical
release versions.
[0153] As indicated in FIG. 6C, examples of before and after
evaluations may include comparisons on a per computational instance
basis for all transactions, ranked by computational instance, per
service basis, or ranked by service. These examples are not
intended to be limiting. A per computational instance comparison
for all transactions could be used, for example, to help diagnose
possible performance degradation of a particular computational
instance following an upgrade. Such an evaluation could allow a
system manager or other personnel to view the overall impact on the
particular computational instance of an upgrade, as well as examine
transaction components that contribute to the overall impact. These
could include before and after comparisons of average server
processing times, database access times, semaphore usage and
queuing times, and network transmission times, among other possible
components. In accordance with example embodiments, the
contributions of each of these components may be determined
according time-averaged response time for transactions associated
with the particular computational instance as captured in the
transaction log 600 over time intervals before and after the
upgrade.
[0154] A per computational instance comparison for all transactions
could be used to provide a vendor or service provider with a
statistical evaluation a particular upgrade's impact across some or
all computational instances. For example, the data could be used to
form an "impact distribution" of the statistical frequency of
impact values as a function of impact value. Such a distribution
could be represented by a histogram. Other forms are possible as
well. Individual computational instances, corresponding to
particular customers or organization, for example, could then be
located on the distribution according to their impact, and compared
with statistical measures, such as mean, median, variance, and so
on.
[0155] An evaluation ranked by computational instance for all URLs
may list computational instances according to the size of the
impact of the upgrade for all URLs. Such an evaluation could be
used, for example, to compare overall performance between
computational instances before and after an upgrade, or to identify
potential problems and/or improvements associated with URL
transactions for computational instances resulting from an upgrade.
A similar evaluation could be carried out on a per-URL basis as
well.
[0156] As with the vertical view, evaluations and comparisons in
the horizontal view may be made based on the service(s) invoked by
particular transactions, the server that carries out transactions,
or system resources that are utilized as part of servicing
transactions. Evaluations may be extended to include other filter
selections as well.
[0157] B. Example Operation
[0158] The discussion above alluded to a system manager or other
personnel engaging in various example comparative performance
evaluations of different software release versions. In accordance
with example embodiments, interactive performance evaluations and
comparisons may be supported by one or more user devices, such as
the user device 612 shown in FIGS. 6A-6C. As indicated, such a
device could be configured remotely from the remote network
management platform 320, or could be integrated as part of a local
platform component. A user at the user device 612 could invoke
various performance evaluation operations and functions on the
computing device 602, for example. Operation of performance
evaluations and comparisons are be illustrated by way of example in
FIGS. 7A-7D, which depict hypothetical graphical "dashboards" of a
graphical user interface (GUI).
[0159] FIG. 7A depicts an example dashboard 700, which may be used
to generate an upgrade performance impact summary, as indicated at
the top. Interactive fields in the dashboard include computational
instance selection, before and after dates selection, and number of
days selection, which together, among other items, may configure a
filter. The dashboard also includes an "execute" button for
applying the filter. The dashboard 700 also includes two high-level
summary sub-windows: a syslog metrics window 702, and "big-data"
("BD") metrics window 704. The term "big data" is just an example
descriptor name that could be used to identify performance results,
such as those stored in the performance data repository 604
described above for example. As shown, the syslog metrics takes the
form of a table that includes columns for metric name, before,
after, and percent difference. The table entries are identified by
metric name (e.g., total response time, count average per minute,
etc.), with values for each column heading from logged transactions
for the selected before and after dates. A similar table in
generated and displayed for the BD metrics 704.
[0160] FIG. 7B depicts an example dashboard 706 for more detailed
comparative performance evaluation. Interactive selection fields in
the dashboard 706 include instance selection 708, before and after
day selection 710, and an execute button, among others. By way of
example, a filter has been set for sorting by URL. Clicking the
execute button causes a table 712 to be generated and displayed. By
way of example the table 712 lists URLs according to impact, and
includes columns for identifying the URLs, the impact, metrics of
total response time (in units of milliseconds), counts, and
component times for server response time, browser time, network
time, and database queries. In example embodiments, there could be
additional columns for additional component, with a horizontal
scroll bar to bring them into the viewing window.
[0161] FIG. 7C reproduces the example dashboard 706, but now shows
an example drop-down menu of the "sort-by" selection 714. This
example illustrates how further and/or alternative refinements and
selection could be configured as part of a filter.
[0162] FIG. 7D depicts an example dashboard 716 that includes
quantitative graphical representations of comparative performance
data in data plots 718. In the illustration, before and after plots
are dashed or solid lines, respectively. An example plot on the
left show transaction counts as a function of time; an example plot
in the middle show response time as a function of time; and an
example plot on the right shows resource usage (e.g., semaphores)
as a function of time.
[0163] A system manager or other performance evaluation personnel
could use these and other graphical tools of a GUI to evaluate a
wide range of performance measures at a high level and/or in
detail. The information determined from such displays could be used
to trouble-shoot performance issues within a computational instance
or across computational instances. A system manager or other
personnel could also address customer queries regarding performance
after an upgrade, for example. Other examples of applying
comparative performance are possible as well.
VI. Example Method
[0164] FIG. 8 is a flow chart illustrating an example embodiment of
a method for comparative performance monitoring and evaluation to
two software release versions, such as a previous version and an
upgraded version. The method illustrated by FIG. 8 may be carried
out by a computing device, such as computing device 100, a cluster
of computing devices, such as server cluster 200, and or computing
device 602. However, the process can be carried out by other types
of devices or device subsystems. For example, the process could be
carried out by a portable computer, such as a laptop or a tablet
device. In an example embodiment, the method illustrated in FIG. 8
may be carried out by a computing device disposed within a remote
network management platform, such as platform 320, which includes
one or more computational instances, each configured to remotely
manage a managed network, such as network 300. Further, the
computing device may be operational to execute a comparative
performance monitoring software application.
[0165] The embodiments of FIG. 8 may be simplified by the removal
of any one or more of the features shown therein. Further, these
embodiments may be combined with features, aspects, and/or
implementations of any of the previous figures or otherwise
described herein.
[0166] Block 800 may involve logging transactions to a database of
the remote network management platform. In accordance with example
embodiments, each transaction may be carried out between one or
more server devices of a computational instance and one or more
client devices associated with a managed network. Further, in
carrying out the transactions, the one or more server devices may
be executing a particular release version of a set of program code
units.
[0167] Block 802 may involve retrieving and analyzing a first set
of transactions that were carried out by a first release version of
the set of program code units to determine a first set of
performance metrics. The first set of performance metrics may
include first time-averaged completion rates of the first set of
transactions for each of a plurality of transaction
classifications. In consideration of the discussion above,
completion rates may also be considered the arithmetic inverse of
response times.
[0168] Block 804 may involve retrieving and analyzing a second set
of transactions that were carried out by a second release version
of the set of program code units to determine a second set of
performance metrics. The second set of performance metrics include
second time-averaged completion rates of the second set of
transactions for each of the plurality of transaction
classifications.
[0169] Block 806 may involve receiving input from a user device
specifying a classification filter to apply to the plurality of
transaction classifications of each of the first and second sets of
performance metrics.
[0170] Finally, block 808 may involve providing, for display on a
graphical user interface (GUI) of the user device, a quantitative
comparison of the filtered first and second sets of performance
metrics.
[0171] In accordance with example embodiments, transactions may
include information indicative of a time-rate of completion for
each transaction, such as an inverse of response times, as
described above. Transactions may also include information
identifying a respective group of one or more web-based
applications associated with each given transaction, and
information indicative of which of the first or second versions of
the set of program code units was operational when the given
transaction was carried out. A non-limiting example of information
identifying a respective group of the one or more web-based
applications is a uniform record locator (URL) of website that
provides one or more services relating to management of the managed
network.
[0172] In accordance with example embodiments, the first
time-averaged completion rates of the first set of transactions may
be time-averaged completion rates of transactions associated with
each of the respective groups of the one or more web-based
applications as implemented by the first version of the set of
program code units. Similarly, the second time-averaged completion
rates of the second set of transactions may be time-averaged
completion rates of transactions associated with each of the
respective groups of the one or more web-based applications as
implemented by the second version of the set of program code units.
Additionally, each of the respective groups of the one or more
web-based applications could correspond to one of the plurality of
transaction classifications, and the quantitative comparison of the
filtered first and second sets of performance metrics could be a
difference between the second and first time-averaged rates for one
or more of the respective groups of the one or more web-based
applications. A difference between time-averaged response times
could be used as well.
[0173] In further accordance of example embodiments, the
quantitative comparison of the filtered first and second sets of
performance metrics further could involve a metric of an impact of
the difference between the second and first time-averaged rates (or
response times) for each of one or more of the respective groups of
the one or more web-based applications. In an example embodiment,
the impact for each of the one or more of the respective groups
could be the difference for the respective group weighted by a
frequency of occurrence of transactions of the respective group,
and each impact could be added to a list of impacts for the
respective groups. Then, a ranking order of the list of impacts
according to relatives sizes of the impacts could be
determined.
[0174] In further accordance with example embodiments, providing
the quantitative comparison of the filtered first and second sets
of performance metrics could entail providing at least a portion of
the list of impacts in ranking order as display elements. In an
example, each display element could correspond to a list entry and
comprising the impact and an identification of the respective group
of the one or more web-based applications associated with the list
entry.
[0175] Also in further accordance with example embodiments, each of
the respective groups of the one or more web-based applications may
be associated with a respective filter category. By way of example,
each respective filter category could be one of: (i) a uniform
record locator (URL) category, (ii) a network management service
category, (iii) a server device identifier category, or (iv) a
network resource category. Then, the ranking order of the list of
impacts according to relatives sizes of the impacts be a ranked
list for a selected one or more of the respective filter
categories, where the ranked list corresponds to a ranking order of
an impact of updating software of the one or more server devices of
the computational instance from the first version to the second
version of the set of program code units. The input specifying the
classification filter comprises selection criteria could then be at
least one of: (i) a filter category, or (ii) a ranking-order
scheme, wherein the ranking-order scheme is one of
largest-to-smallest, smallest-to-largest, or histogrammed.
[0176] In further accordance with example embodiments, the example
method may further entail writing the first and second sets of
performance metrics to a performance data repository, such as the
performance data repository 604.
[0177] In further accordance with example embodiments, the example
method may further entail logging additional transactions to the
database of the remote network management platform. In accordance
with example embodiments, each additional transaction may be
carried out between one or more server devices of at least one
additional computational instance and one or more client devices
associated with a respective additional managed network. Further,
in carrying out the additional transactions, the one or more server
devices may be executing a given release version of a set of
program code units. Additionally, the example method may still
further entail retrieving and analyzing an additional first set of
the additional transactions that were carried out by the first
version of the set of program code units to determine an additional
first set of performance metrics, and retrieving and analyzing an
additional second set of the additional transactions that were
carried out by the second version of the set of program code units
to determine an additional second set of performance metrics. The
additional first set of performance metrics may include additional
first time-averaged completion rates of the additional first set of
transactions for each of the plurality of transaction
classifications, and the additional second set of performance
metrics may include additional second time-averaged completion
rates of the additional second set of transactions for each of the
plurality of transaction classifications.
[0178] The example method may then also entail receiving input from
the user device specifying a further classification filter to apply
to the plurality of transaction classifications of each of: (i) an
aggregate of the first set of performance metrics and the
additional first set of performance metrics, and (ii) an aggregate
of the second set of performance metrics and the additional second
set of performance metrics. A quantitative comparison of the
filtered aggregate first and aggregate second sets of performance
metrics may then be provide for display on the GUI of the user
device.
VII. Conclusion
[0179] The present disclosure is not to be limited in terms of the
particular embodiments described in this application, which are
intended as illustrations of various aspects. Many modifications
and variations can be made without departing from its scope, as
will be apparent to those skilled in the art. Functionally
equivalent methods and apparatuses within the scope of the
disclosure, in addition to those described herein, will be apparent
to those skilled in the art from the foregoing descriptions. Such
modifications and variations are intended to fall within the scope
of the appended claims.
[0180] The above detailed description describes various features
and operations of the disclosed systems, devices, and methods with
reference to the accompanying figures. The example embodiments
described herein and in the figures are not meant to be limiting.
Other embodiments can be utilized, and other changes can be made,
without departing from the scope of the subject matter presented
herein. It will be readily understood that the aspects of the
present disclosure, as generally described herein, and illustrated
in the figures, can be arranged, substituted, combined, separated,
and designed in a wide variety of different configurations.
[0181] With respect to any or all of the message flow diagrams,
scenarios, and flow charts in the figures and as discussed herein,
each step, block, and/or communication can represent a processing
of information and/or a transmission of information in accordance
with example embodiments. Alternative embodiments are included
within the scope of these example embodiments. In these alternative
embodiments, for example, operations described as steps, blocks,
transmissions, communications, requests, responses, and/or messages
can be executed out of order from that shown or discussed,
including substantially concurrently or in reverse order, depending
on the functionality involved. Further, more or fewer blocks and/or
operations can be used with any of the message flow diagrams,
scenarios, and flow charts discussed herein, and these message flow
diagrams, scenarios, and flow charts can be combined with one
another, in part or in whole.
[0182] A step or block that represents a processing of information
can correspond to circuitry that can be configured to perform the
specific logical functions of a herein-described method or
technique. Alternatively or additionally, a step or block that
represents a processing of information can correspond to a module,
a segment, or a portion of program code (including related data).
The program code can include one or more instructions executable by
a processor for implementing specific logical operations or actions
in the method or technique. The program code and/or related data
can be stored on any type of computer readable medium such as a
storage device including RAM, a disk drive, a solid state drive, or
another storage medium.
[0183] The computer readable medium can also include non-transitory
computer readable media such as computer readable media that store
data for short periods of time like register memory and processor
cache. The computer readable media can further include
non-transitory computer readable media that store program code
and/or data for longer periods of time. Thus, the computer readable
media may include secondary or persistent long term storage, like
ROM, optical or magnetic disks, solid state drives, compact-disc
read only memory (CD-ROM), for example. The computer readable media
can also be any other volatile or non-volatile storage systems. A
computer readable medium can be considered a computer readable
storage medium, for example, or a tangible storage device.
[0184] Moreover, a step or block that represents one or more
information transmissions can correspond to information
transmissions between software and/or hardware modules in the same
physical device. However, other information transmissions can be
between software modules and/or hardware modules in different
physical devices.
[0185] The particular arrangements shown in the figures should not
be viewed as limiting. It should be understood that other
embodiments can include more or less of each element shown in a
given figure. Further, some of the illustrated elements can be
combined or omitted. Yet further, an example embodiment can include
elements that are not illustrated in the figures.
[0186] While various aspects and embodiments have been disclosed
herein, other aspects and embodiments will be apparent to those
skilled in the art. The various aspects and embodiments disclosed
herein are for purpose of illustration and are not intended to be
limiting, with the true scope being indicated by the following
claims.
* * * * *